Averra Extract MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable behavioral context beyond annotations: it explains that usage is counted per user across all API keys (not per key), cached requests count against quota, and counters reset at the start of each calendar month (UTC). While annotations cover safety (read-only, non-destructive, idempotent, open-world), the description provides operational details that help the agent understand quota mechanics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (description, args, returns, examples, error handling) and front-loaded key information. While comprehensive, some sections like the detailed return format examples could be slightly condensed, but overall it's efficient and organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only quota checking tool with comprehensive annotations and a simple parameter schema, the description provides excellent contextual completeness. It includes purpose, usage guidelines, behavioral details, parameter documentation, return format examples, and error handling—all without needing an output schema since return values are clearly described.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema already fully documents the single optional parameter (response_format with enum values and default). The description repeats this information in the 'Args' section but doesn't add significant semantic value beyond what's in the schema, meeting the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Check the current month's Extract API usage and remaining quota for the authenticated account.' It specifies the exact resource (Extract API usage/remaining quota) and timeframe (current month), distinguishing it from sibling tools like key management or extraction tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage examples with 'Use when:' statements, including scenarios like checking remaining quota, confirming quota before batch operations, and identifying the user's plan. It gives clear context for when to invoke this tool versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.